Masters Degrees (Civil Engineering)

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    Development of a Floating Car Data (FCD) model to evaluate traffic congestion : a case of Kampala, Uganda
    (Stellenbosch : Stellenbosch University, 2023-03) Nalubega, Sharifa Ishaq; Andersen, Simen Johann; Andersen, Simen Johann; Stellenbosch University. Faculty of Engineering. Dept. of Civil Engineering.
    ENGLISH ABSTRACT: Traffic congestion remains a stumbling block in an efficient and accessible road network. Attempts have been investigated to monitor congested areas and propose mitigation measures to alleviate the issue. However, transport planning models, such as the four-step traditional models, are expensive and complex. This research develops a novel floating car data (FCD) model similar to the traditional model but is more cost-effective and efficient for transport planning. Many African cities cannot afford complex planning models, but the need to improve road networks remains indisputable. Using FCD's cost-effective traffic data collection strategy, this research proposes a model designed to monitor and thus alleviate city traffic congestion. This study focuses on a novel FCD model for evaluating traffic congestion in developing African countries like Uganda. This research aims to contribute to alleviating traffic congestion in African cities by exploiting FCD. The methodology adopted to achieve this was developing a novel FCD model. This study utilized traffic speeds and travel times during peak and off-peak hours to determine the congestion intensities in different sections of Kampala. The speed reduction index (SRI) was used to classify the congestion levels into no, low, and high congestion areas. Delay rates were used to determine the varying delays in different city areas. Then, PTV VISUM software was utilized to develop a road network model and visualize the varying intensities of congestion. Then, two highly ranked zones in terms of delay rates were analysed to ascertain the causes. The causes were mainly high volumes of vehicles on the major arterials, non-operational traffic lights, and social and economic hubs in the adjacent areas of those zones. This study further proposed mitigation measures using the PTV VISSIM software by conducting a simulation analysis. When signal timings were altered, the simulation indicated a 42% reduction in vehicle delay on the major route at the intersection in zone 13. The research concluded that African cities could embrace technological advancement in traffic statistics and improve their cities.
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    A regional characterisation and calibration of load effects for normal and abnormal vehicles for the structural design of highway bridges in South Africa
    (Stellenbosch : Stellenbosch University, 2023-03) Jacques, Van Rooyen; Pierre Francois, Van der Spuy; Gideon, Van Zijl; Stellenbosch University. Faculty of Engineering. Dept. of Civil Engineering.
    ENGLISH ABSTRACT: The Technical Methods for Highways 7 (TMH7) is the current traffic load model used to design highway bridges in South Africa. Road traffic has changed significantly since its last revision in 1988. Van der Spuy (2020) recognised the shortcomings of the TMH7 and derived a new traffic load model based on weigh-in-motion data collected on the National Route 3 (N3) in Kwa-Zulu Natal. This study forms part of ongoing research to update the bridge design codes in South Africa. This study sets out to refine the traffic load model derived by Van der Spuy (2020) on a regional basis. Additionally, this study investigates the influence of abnormal vehicle occurrence on the magnitudes of the characteristic load effects. The goal of this study is a regionally calibrated traffic load model that makes provision for the influence of abnormal vehicle occurrence. Two primary investigations are conducted to achieve the goal of this study. The first investigation quantifies the interprovincial variation of traffic load effect magnitudes by comparing the characteristic load effects from regions throughout South Africa. It is found that considerable interprovincial variation exists. The magnitude of hogging moments in KwaZulu-Natal is 1.8 and 1.6 times that of the Eastern Cape and Northern Cape provinces, respectively, and shear forces in the Gauteng province up to 1.9 times as high as the load effects in the Northern Cape. The lowest degree of interprovincial variation is found for sagging moments. The substantial interprovincial variation motivates the derivation of load effect adjustment factors applied to the existing load model of Van der Spuy (2020) to account for this variation. The load effect adjustment factors are complimented by reliability-based partial factors, which were also determined for the respective provinces. The second set of investigations was conducted to establish the influence of normal and abnormal vehicles on the load effect magnitudes. Protocols were established to separate normal and abnormal vehicles. The protocols were primarily based on South African legislation that road traffic must adhere to, in conjunction with assumptions regarding the number of axles. These investigations were separated into two sets of investigations. Firstly, the focus was on the influence of Normal vehicles on the critical load effects. It also included implicit investigations relating to abnormal vehicles on the critical load effects. Additionally, the concept of threshold vehicles was introduced and showed the complexities involved in distinguishing between normal, permit, and illegally overloaded and abnormal vehicles. After the exclusive investigations into normal vehicles, investigations were conducted on abnormal vehicles exclusively. The first step was to separate abnormal vehicles based on the number of axles. It resulted in two primary abnormal vehicle types: eight and nine-axle abnormal vehicles. Abnormal vehicle subclasses were identified based on the modality of the static load effect histograms for eight and nine-axle abnormal vehicles, respectively. Each mode was assumed to represent a unique, abnormal vehicle type (subclass). The individual modes (each representing unique, abnormal vehicle types) were isolated and fit with normal distribution. This was done by employing Gaussian mixture modelling. Characteristic load effects were determined by evaluating the normal distributions associated with each subclass at the 95th percentile. The results of this study showed that significant interprovincial variation exists in the magnitudes of traffic load effects and motivated the use of load effect adjustment factors to account for the variation. The investigations regarding normal and abnormal vehicles suggest that if a separate load model is derived to model abnormal traffic, it should be calibrated on a case-specific basis. However, the investigations related to a separate load model for abnormal vehicles do not indicate a clear requirement for a separate load model to model abnormal vehicles when designing bridges in South Africa.
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    Using drones to improve the quality control of masonry in affordable housing construction.
    (Stellenbosch : Stellenbosch University, 2023-02) Ruthven, Pieter Gerhard; Wium, Jan Andries; Stellenbosch University. Faculty of Engineering. Dept. of Civil Engineering.
    ENGLISH ABSTRACT: The increasing housing backlog present in South Africa has resulted in the need for housing to be delivered with urgency. Attempting to deliver houses in a quantitative manner has led to quality being overlooked, although key role players have made it clear that there should be a shift towards qualitative delivery of housing. Research has found that quality concerns are recurring in affordable housing projects and therefore the need for improved quality control is evident. Motivated by the need to address these recurring problems, this study aims to investigate using a drone to improve the quality control on affordable housing projects, limited to the quality control of masonry works. Two affordable housing projects in the Western Cape were investigated over a period of two years to assist with achieving the aim of this study. The study seeks to improve aspects of dimensional quality control by firstly identifying the quality concerns on these projects by using traditional defect identification methods. A total of 1 048 measurements were taken on High Density Double Story (HDDS) units and 336 on Stand Alone Single Story (SASS) units. These measurements are compared to the specified dimensional requirements to identify which measurements do not adhere to the defined standards. Valuable findings are obtained and summarized from the data obtained using the traditional method. The study then investigates using a drone through 2D and 3D analysis to find a practical and effective manner through which dimensional aspects of quality control can be improved. Measurements taken from 2D images are compared to the actual measurements taken on site to determine the accuracy and effectiveness of using a drone in this manner. A 3D model of a housing unit is then developed through photogrammetry and accompanying software to assist with quality control through defect identification. The practicality and effectiveness of both methods are discussed by comparing them to the traditional method. From these findings it was determined that neither method would be practical and instead site progress monitoring through use of a drone is suggested to improve the dimensional aspects of quality control. A framework is then put forward as a recommendation to implement a drone on an affordable housing project.
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    Application of data mining and machine learning on occupational health and safety struck-by incidents on south African construction sites: a CRISP-DM approach.
    (Stellenbosch : Stellenbosch University, 2023-02) Adams, Logan Charl; Wium, Jan Andries; Stellenbosch University. Faculty of Engineering. Dept. of Civil Engineering.
    ENGLISH ABSTRACT: Occupational Health and Safety in the South African construction industry face many performance challenges that result in potentially avoidable incident occurrences. The study aims to propose the utilisation of data mining and classification machine learning models to improve data understanding, promote knowledge and information extraction, and encourage prediction capabilities through classification methods. A mixed research approach was applied in the study to enable a holistic usage of data and its applications. Interviews (qualitative research component) allowed the identification of the current state of OHS data and data management in the South African construction industry while identifying data considerations for the quantitative research component (Exploratory Data Analysis and classification machine learning models). Data sourced from Federated Employers Mutual Assurance Company (an insurance database), and additional databases (sourced from the Federal Reserve Bank of St. Louis and Organisation for Economic Cooperation and Development), enabled a quantitative Exploratory Data Analysis and the development of multiple classification machine learning models. The Exploratory Data Analysis provided insights into data understanding and the potential of using it to enable datadriven safety decision-making. The classification models provided insights into the possibility of an industry-wide classification prediction model based on existing data while also providing valuable insights into the fundamental concerns and limitations. The qualitative and quantitative components of the study highlighted several concerns regarding data, data management, and data innovations across OHS in the South African construction industry. At the core was the lack of understanding regarding the possibilities of data and the misaligned value proposition witnessed. Furthermore, the notable limitations in the quality of data and the mechanisms that influence its quality were highlighted, including the effects of ineffective incident investigations for fact-finding and prominent underreporting experienced in the construction industry. Data mining and machine learning offered the ability to extract deeper insights from incidents and enable improvements in OHS performance through data-driven safety decision-making. Three output variables were evaluated across several machine learning algorithms in terms of the model's ability to successfully predict and classify the state of an incident namely (1) Injury Location (the physical injury location on the affected individual's body), (2) Nature of Injury (the type of injury the affected individual experienced), and (3) Days off (number of days required off from work for recovery). The results obtained from the machine learning models demonstrate the capability to predict the Days off variable to high accuracy levels (average of 81.8%), moderate accuracy levels for the Nature of Injury (average of 37.4%), and low accuracy levels for Injury Location (average of 17.8%). The performance Stellenbosch University iii | P a g e of the various machine learning models are directly influenced by the underlying correlation between the output and input variables and the number of classifications required within the output variable itself – with the largest correlation coefficient and the number of classifications respectively noted as Injury Location (0.07, 20), Nature of Injury (0.14, 9), and Days off (0.07, 3). It is recommended that the successful implementation of data mining and machine learning requires collaborative efforts between the industry, Government, and academia.
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    UAV-based track bed inspection and monitoring
    (Stellenbosch : Stellenbosch University, 23-01) van Tonder, Francois Johann; Croukamp, Leon; Conradie, Pieter; Stellenbosch University. Faculty of Engineering. Dept. of Civil Engineering.
    ENGLISH ABSTRACT: Transportation is a fundamental pillar of economic activities in a country. Therefore, it is important to ensure that transport systems are functional, reliable, and e_cient. Passenger rail is growing in demand due to population growth, congested roads, and the need for sustainable transportation. Railway maintenance plays an essential role in keeping the railway infrastructure safe, reliable and e_cient. To keep up with the growing demand for rail transportation, new technologies and techniques must be implemented to improve maintenance activities. Unmanned Aerial Vehicles (UAVs) are widely used in industries such as oil and gas, construction, mining, and agriculture to improve project e_ciency, cost savings, and safety. However, research on the use of UAVs for inspection and monitoring of railway infrastructure is still emerging. The purpose of this research is to determine whether UAVs are a viable solution for inspection and monitoring of rail trackbeds. This research is based on a mixed-method approach. An in-depth review of the literature covers the components of the railway infrastructure and their respective failure types, the UAV industry as a whole, and _nally, current use of UAVs in the railway industry. On the basis of the review of literature, a UAV-based trackbed inspection and monitoring framework was created. To justify the practicality of the framework, it was tested in a case study. The case study was conducted in two sections of the railway in the Western Cape of South Africa. Part one was done on the PRASA line between Stellenbosch Station and Du Toit Station in Stellenbosch. Part two was done on a TRANSNET line passing through Sir Lowry's Pas. To perform weekly, monthly and specialised inspections a multi-rotor drone was used. To process the data for monthly inspections, Pix4Dmapper and Pix4Dcloud Advanced were used. The results of each weekly and monthly inspection were exported to an Excel workbook and summarized. The specialised inspection required further rock fall analysis in Rocscience RocFall 3. Key _ndings of the UAV-based trackbed inspection and monitoring framework from the case study include the following. Minimized on-site time, increased safety, streamlined workow, and repeated models for track-bed monitoring. In addition, it was found that this method provides an e_cient solution for increasing the frequency of track-bed inspections, provides rapid results after adverse weather events, and helps identify geotechnical risks from the surrounding environment. It is recommended that this research continue on a larger scale, using up-to-date technology such as infrared cameras and UAV-based LiDAR, and incorporating other infrastructure components into the framework. However, the _nal conclusion of the study is that a UAV-based method has enormous potential to increase railway trackbed safety and reliability, while simultaneously improving the safety of inspection personnel.